Auto associative memory solved example with Mistaken and Missing data

#softcomputing #algorithm #neuralnetwork #datamining #machinelearning
Autoassociative memory network with solved question on missing and mistaken data entries
Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types
of activation function, Network architectures, Knowledge representation, Hebb net
1.2 Learning processes: Supervised learning, Unsupervised learning and
Reinforcement learning
1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta
Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule
1.4 Applications and scope of Neural Networks
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Supervised Learning Networks :
2.1 Perception Networks - continuous & discrete, Perceptron convergence theorem,
Adaline, Madaline, Method of steepest descent, - least mean square algorithm,
Linear & non-linear separable classes & Pattern classes,
2.2 Back Propagation Network,
2.3 Radial Basis Function Network.
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Unsupervised learning network:
3.1 Fixed weights competitive nets,
3.2 Kohonen Self-organizing Feature Maps, Learning Vector Quantization,
3.3 Adaptive Resonance Theory - 1
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Associative memory networks:
4.1 Introduction, Training algorithms for Pattern Association,
4.2 Auto-associative Memory Network, Hetero-associative Memory Network,
Bidirectional Associative Memory,
4.3 Discrete Hopfield Networks.
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Fuzzy Logic:
5.1 Fuzzy Sets, Fuzzy Relations and Tolerance and Equivalence
5.2 Fuzzification and Defuzzification
5.3 Fuzzy Controllers

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[@627bhupeshsingh2]

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[@ramyasrichennu276]

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[@AnkitSharma-id5je]

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[@mani_fitness03]

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[@dhruvsompura5487]

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[@utkarshjalan2675]

you should share a soft copy of the question and answers.

[@mohammadanas6755]

Make vedio on ART1(ADAPTIVE RESONANCE THEORY 1)